Bioarrays
From Basics to Diagnostics
1st Edition
Published on 4. October 2007
XVIII, 269 pages
978-1-59745-328-8 (ISBN)
Description
Bioarrays: From Basics to Diagnostics provides an integrated and comprehensive collection of timely articles on the use of bioarray techniques in the fields of biotechnology and molecular medicine. The entire volume is broken into four sections - Bioarray Technology Platforms, Biomarkers and Clinical Genomics, Biomarker Identification Using Clinical Proteomics and Glycomics, and Emerging Technologies in Diagnostics - that create one well-integrated work. Particular emphasis is placed on DNA, protein, and carbohydrate biochips. The volume also looks extensively at oligonucleotides, cDNA, proteins, antibodies, and carbohydrate arrays.
Bioarrays: From Basics to Diagnostics will serve as an indispensable reference for graduate students, post-docs, and professors as well as an explanatory analysis for executives and scientists in biotechnology and pharmaceutical companies.
Reviews / Votes
From the reviews:
"This book compiles various array techniques as applied to profiling of disease states. Included are DNA, protein, and carbohydrate-based methods. . is directed at working scientists interested in the application of array procedures for diagnostic uses. Research fellows and laboratory heads might find material of interest. The editor has selected a broad range of collaborating authors with extensive background in this area. . Each chapter is accompanied by a useful bibliography." (Eugene A Davidson, Doody's Review Service, October, 2007)
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06/2007
Humana Press Inc.
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Content
Bioarray Technology Platforms.- Investigation of Tumor Metastasis by Using cDNA Microarrays.- From Tissue Samples to Tumor Markers.- Experimental Design for Gene Expression Analysis.- From Microarrays to Gene Networks.- Biomarkers And Clinical Genomics.- Reduction in Sample Heterogeneity Leads to Increased Microarray Sensitivity.- Genomics to Identify Biomarkers of Normal Brain Aging.- Gene Expression Profiling for Biomarker Discovery.- Array-Based Comparative Genomic Hybridization.- Regional Specialization of Endothelial Cells as Revealed by Genomic Analysis.- Biomarker Identification by Using Clinical Proteomics and Glycomics.- Identification of Target Antigens in CNS Inflammation by Protein Array Technique.- Differential Protein Expression, Protein Profiles of Human Gliomas, and Clinical Implications.- Antibody-Based Microarrays.- Glycoprofiling by DNA Sequencer-Aided Fluorophore-Assisted Carbohydrate Electrophoresis.- High-Throughput Carbohydrate Microarray Technology.- Emerging Technologies in Diagnostics.- Microarrays and Blood Diagnostics.- "Lab-on-a-Chip" Devices for Cellular Arrays Based on Dielectrophoresis.- Genetic Disorders and Approaches to Their Prevention.
5 Reduction in Sample Heterogeneity Leads to Increased Microarray Sensitivity (S. 61-62)
Amanda J. Williams, Kevin W. Hagan, Steve G. Culp, Amy Medd, Ladislav Mrzljak, Tom R. Defay, and Michael A. Mallamaci
Summary
DNA microarrays are most useful for pharmacogenomic discovery when a clear relationship can be made between gene expression in a targeted tissue and drug affect. Unfortunately, the true target of the drug affect is most often a subpopulation of cells within the tissue. Thus, when heterogeneous tissues containing many diverse cell types are profiled, expression changes, especially in low-abundance genes, are often obscured. In this chapter, two examples are presented where a cellular subpopulation is isolated from its complex background, with minimal cellular activation, resulting in increased microarray detection sensitivity. In the first example, erythrocytes (the most abundant cell population in blood) were removed or whole blood was immediately stabilized before RNA isolation. The removal of erythrocytes resulted in a twofold increase in the detectability of leukocyte-specific genes. During the study, protocols for RNA isolation from rat blood were validated. In addition, a list of 91 genes was generated whose expression correlated with the level of erythrocyte contamination in rat blood. In the second example, laser microbeam microdissection (LMM) was used to isolate a specific neuronal population. Our LMM amplification technique was first validated for reproducibility. After validation, data obtained from pooled neurons, cortical tissue slices, and whole brain were compared. Overall, 20% of the transcripts detected in whole brain and 13% of the transcripts detected in tissue slices were not detected in LMM neurons. Many of these transcripts were specific to neuroglial support cells or noncortical neurons, verifying that our LMM technique captured only the neurons of interest. Conversely, 10% of the transcripts detected in LMM neurons were not detected in cortical tissue slices, and 14% were not detected in whole brain. As expected, these transcripts were neuronal specific and were presumably still present in the broader tissue regions. However, in neurons isolated by LMM, the effective concentration of these previously undetectable transcripts was raised because of the elimination of competing signal noise from extraneous cell types, reinforcing the claim that microdissection can be used to increase microarray sensitivity.
Key Words: Brain, blood, detection sensitivity, DNA microarray, erythrocyte, laser capture microdissection, leukocyte, neuron.
1. Introduction
Gene expression profiling by using DNA microarrays has become an integral part of basic and applied research in both the academic and industrial scientific communities. This technology has been successfully used for many distinct applications ranging from disease classification and functional genomics to pharmacogenomics biomarker identification and single-nucleotide polymorphism analysis (1). Because of the relatively large quantity of starting material necessary for microarray use, initial studies primarily focused on animal disease models or cultured cells. Profiling experiments on human tissue required macrodissected regions to generate sufficient starting material. Unfortunately, owing to the heterogeneous mixture of cells present in complex tissues, it is difficult for such studies to detect genes that are expressed at low levels or within rare subpopulations. When genes are detectable, it is difficult to compare the relative levels of gene expression between two or more samples. Part of the problem is the extraneous signal noise contributed by cell types that do not express the genes of interest.
In addition, variability in the cellular composition of each sample can obscure changes that are occurring within one cell type. Recent technical advances in small-scale RNA isolation and amplification, laser microdissection, and RNA stabilization have now made it possible to stratify, with minimal cellular activation, specific cell populations within complex tissue samples. Thus, the expression profiles of cellular subpopulations previously lost in the transcriptional complexity of heterogeneous tissues can now be uncovered. In this chapter, we provide two examples in which the reduction of biological heterogeneity within a sample is accompanied by an increase in gene expression detectability by using microarrays. In the first example, the advantages and disadvantages of reducing cellular heterogeneity in whole blood are explored.
Amanda J. Williams, Kevin W. Hagan, Steve G. Culp, Amy Medd, Ladislav Mrzljak, Tom R. Defay, and Michael A. Mallamaci
Summary
DNA microarrays are most useful for pharmacogenomic discovery when a clear relationship can be made between gene expression in a targeted tissue and drug affect. Unfortunately, the true target of the drug affect is most often a subpopulation of cells within the tissue. Thus, when heterogeneous tissues containing many diverse cell types are profiled, expression changes, especially in low-abundance genes, are often obscured. In this chapter, two examples are presented where a cellular subpopulation is isolated from its complex background, with minimal cellular activation, resulting in increased microarray detection sensitivity. In the first example, erythrocytes (the most abundant cell population in blood) were removed or whole blood was immediately stabilized before RNA isolation. The removal of erythrocytes resulted in a twofold increase in the detectability of leukocyte-specific genes. During the study, protocols for RNA isolation from rat blood were validated. In addition, a list of 91 genes was generated whose expression correlated with the level of erythrocyte contamination in rat blood. In the second example, laser microbeam microdissection (LMM) was used to isolate a specific neuronal population. Our LMM amplification technique was first validated for reproducibility. After validation, data obtained from pooled neurons, cortical tissue slices, and whole brain were compared. Overall, 20% of the transcripts detected in whole brain and 13% of the transcripts detected in tissue slices were not detected in LMM neurons. Many of these transcripts were specific to neuroglial support cells or noncortical neurons, verifying that our LMM technique captured only the neurons of interest. Conversely, 10% of the transcripts detected in LMM neurons were not detected in cortical tissue slices, and 14% were not detected in whole brain. As expected, these transcripts were neuronal specific and were presumably still present in the broader tissue regions. However, in neurons isolated by LMM, the effective concentration of these previously undetectable transcripts was raised because of the elimination of competing signal noise from extraneous cell types, reinforcing the claim that microdissection can be used to increase microarray sensitivity.
Key Words: Brain, blood, detection sensitivity, DNA microarray, erythrocyte, laser capture microdissection, leukocyte, neuron.
1. Introduction
Gene expression profiling by using DNA microarrays has become an integral part of basic and applied research in both the academic and industrial scientific communities. This technology has been successfully used for many distinct applications ranging from disease classification and functional genomics to pharmacogenomics biomarker identification and single-nucleotide polymorphism analysis (1). Because of the relatively large quantity of starting material necessary for microarray use, initial studies primarily focused on animal disease models or cultured cells. Profiling experiments on human tissue required macrodissected regions to generate sufficient starting material. Unfortunately, owing to the heterogeneous mixture of cells present in complex tissues, it is difficult for such studies to detect genes that are expressed at low levels or within rare subpopulations. When genes are detectable, it is difficult to compare the relative levels of gene expression between two or more samples. Part of the problem is the extraneous signal noise contributed by cell types that do not express the genes of interest.
In addition, variability in the cellular composition of each sample can obscure changes that are occurring within one cell type. Recent technical advances in small-scale RNA isolation and amplification, laser microdissection, and RNA stabilization have now made it possible to stratify, with minimal cellular activation, specific cell populations within complex tissue samples. Thus, the expression profiles of cellular subpopulations previously lost in the transcriptional complexity of heterogeneous tissues can now be uncovered. In this chapter, we provide two examples in which the reduction of biological heterogeneity within a sample is accompanied by an increase in gene expression detectability by using microarrays. In the first example, the advantages and disadvantages of reducing cellular heterogeneity in whole blood are explored.
